Cognitive-Aware Complexity Management of Large-Scale Conceptual Models
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  Guylerme Figueiredo   Guylerme V. S. Figueiredo
System Analyst


Wednesday, April 20, 2016
07:30 AM - 08:15 AM

Level:  Intermediate

As reported in the literature, one of the key problems of conceptual modeling in critical domains is the cognitive intractability of the resulting models. Given the typical intrinsic complexity of these domains and the expressivity required by the models so that they can really serve as precise characterizations of the domains at hand, these models tend to rapidly grow in size and in the level of detail to contain thousands of classes, relations, generalization partitions, etc. Our objective is to build on existing literature on clustering, relevance, summarization and filtering methods. In our approach instead, by relying on a conceptual modeling language that contains rich ontological foundation, we can leverage on natural domain-independent mechanism for viewpoint selection, clustering, relevance determination and abstraction. In the sequel, we elaborate on four complementary mechanisms for cognitive complexity management of large conceptual models.

Ph.D Candidate on Computer Science (Federal University of Espirito Santo). Computer Science Master (Pontífice Universidade Católica - Rio de Janeiro - 2012). System Analyst at Petrobras since 2006, works with Information Management, Conceptual Modeling and Ontologies.

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